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Reproducible, Unbiased Ligand Identification Assisted by Artificial Intelligence and Development of Ligand Reference Libraries

人工智能辅助的可重复、公正的配体鉴定和配体参考文库的开发

基本信息

批准号：
10432049
负责人：
WLADEK MINOR
金额：
$ 56.12万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-17 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10432049
关键词：
Address Advisory Committees Algorithms Artificial Intelligence Benchmarking Binding Sites Bioinformatics Biological Biology Biomedical Research Categories Cognitive Complex Cryoelectron Microscopy Crystallization Data Data Set Descriptor Development Disease Docking Drug Design Drug Targeting Effectiveness Electron Microscopy Ensure FAIR principles Generations Human Intuition Ions Libraries Ligand Binding Ligands Machine Learning Methodology Methods Modeling Molecular Molecular Conformation Molecular Structure Nucleic Acid Binding Ontology Peptides Pharmaceutical Preparations Pharmacotherapy Proteins Protocols documentation Public Health Recommendation Reproducibility Research Resolution Resources Roentgen Rays Software Tools Standardization Stratification Structural Models Structure System Teaching Materials Techniques Training Uncertainty Update Validation X-Ray Crystallography base computational chemistry density drug discovery electron density experience improved inhibitor machine learning algorithm macromolecule novel online resource simulation small molecule software development stem three dimensional structure tool

项目摘要

Our current understanding of the molecular mechanisms of disease and structure-based design of drugs for treatment, rely on experimentally determined 3D structures of proteins and other macromolecules complexed with small molecule ligands. Many of these structures have direct relevance to public health, especially complexes of drug targets with drugs, inhibitors, substrates, or allosteric effectors. Yet, structure-based drug discovery is severely complicated and hindered by experimental bias and the shortcomings of current methods of experimental ligand identification, which often result in misidentified, missing, or misplaced ligands. The propagation of erroneous structures combined with an increased accessibility to structural data not only thwarts reproducibility in biomedical research and drug discovery, but also diverts valuable resources down doomed research avenues. We will leverage our extensive experience validating and refining ligand binding sites to generate ligand reference libraries that will be made publically available on a new web resource dedicated to the interaction of small molecules and macromolecules. These libraries can be used in many downstream applications, such as drug design, computational chemistry, biology, and bioinformatics. We will utilize recent technological advances in machine learning in conjunction with existing tools to create a standardized protocol for density interpretation and unbiased, reproducible ligand identification. This pipeline will not only be able identify and model ligands in unassigned density fragments, but also be able to detect and correct suboptimally refined ligands in existing structures. As the proposed AI will be free from cognitive bias, it should alleviate the most severe problems in structure-based drug design. Because improperly interpreted structures can have a significant deleterious ripple effect, we will experimentally verify select biomedically important structures with dubious experimental support for critical small molecules using use X-ray crystallography or electron microscopy.

我们目前对疾病分子机制的理解和基于结构的设计用于治疗的药物，依赖于实验确定的蛋白质和其他大分子与小分子配体形成络合。许多这样的结构都有直接的与公众健康有关，特别是药物靶标与药物、抑制剂、底物、或变构效应器。然而，基于结构的药物发现是极其复杂和困难的。由实验偏倚和现行实验配基方法的不足识别，这往往导致识别错误、丢失或放错了配基。传播错误的结构与增加的结构数据的可访问性相结合，不仅阻碍了生物医学研究和药物发现的重复性，但也转移了有价值的资源枯竭注定是一条研究之路。我们将利用我们丰富的经验来验证以及提纯配基结合位点以生成将公开制作的配基参考库可在一个新的网络资源上获得，该资源致力于小分子和大分子。这些文库可用于许多下游应用，如药物设计、计算化学、生物学和生物信息学。我们将利用最近的机器学习中的技术进步与现有工具相结合，以创建密度解释的标准化协议和无偏见、可重复的配基身份证明。这条管道将不仅能够识别和模拟未分配密度的配体片段，但也能够检测和纠正现有的次优精炼配体结构。由于拟议的人工智能将不存在认知偏见，它应该会缓解最严重的基于结构的药物设计中存在的问题。因为未正确解释的结构可能具有显著的有害涟漪效应，我们将通过实验验证选定的生物医学重要利用X射线对关键小分子具有可疑实验支持的结构结晶学或电子显微镜。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Rapid response to emerging biomedical challenges and threats.

DOI：
10.1107/s2052252521003018
发表时间：
2021-05-01
期刊：
IUCrJ
影响因子：
3.9
作者：
Grabowski M;Macnar JM;Cymborowski M;Cooper DR;Shabalin IG;Gilski M;Brzezinski D;Kowiel M;Dauter Z;Rupp B;Wlodawer A;Jaskolski M;Minor W
通讯作者：
Minor W

Recognizing and validating ligands with CheckMyBlob.

DOI：
10.1093/nar/gkab296
发表时间：
2021-07-02
期刊：
Nucleic acids research
影响因子：
14.9
作者：
Brzezinski D;Porebski PJ;Kowiel M;Macnar JM;Minor W
通讯作者：
Minor W

Covid-19.bioreproducibility.org: A web resource for SARS-CoV-2-related structural models.

DOI：
10.1002/pro.3959
发表时间：
2021-01
期刊：
Protein science : a publication of the Protein Society
影响因子：
0
作者：
Brzezinski D;Kowiel M;Cooper DR;Cymborowski M;Grabowski M;Wlodawer A;Dauter Z;Shabalin IG;Gilski M;Rupp B;Jaskolski M;Minor W
通讯作者：
Minor W

Structural biology and public health response to biomedical threats.

DOI：
10.1063/4.0000186
发表时间：
2023-05
期刊：
STRUCTURAL DYNAMICS-US
影响因子：
2.8
作者：
Lenkiewicz, Joanna;Bijak, Vanessa;Poonuganti, Shrisha;Szczygiel, Michal;Gucwa, Michal;Murzyn, Krzysztof;Minor, Wladek
通讯作者：
Minor, Wladek

Continuous Validation Across Macromolecular Structure Determination Process.

整个大分子结构测定过程的连续验证。

DOI：
10.5940/jcrsj.65.10
发表时间：
2023
期刊：
Nihon Kessho Gakkai shi
影响因子：
0
作者：
Bijak,Vanessa;Gucwa,Michal;Lenkiewicz,Joanna;Murzyn,Krzysztof;Cooper,DavidR;Minor,Wladek
通讯作者：
Minor,Wladek